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ConCur: Knowledge Base Construction and Curation

ConCur：知识库构建和管理

基本信息

批准号：
EP/V050869/1
负责人：
Ian Horrocks
金额：
$ 144.12万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV050869%2F1
关键词：
ConCur Knowledge Base Construction Curation

项目摘要

Knowledge graphs are graph-structured knowledge resources which are often expressed as triples such as ("UK", "hasCapital", "London") and ("London", "instanceOf", "City"). As well as such basic "facts", knowledge graphs often include structural knowledge about the domain, typically based on a hierarchy of entity types (AKA classes or concepts); e.g., ("City", "subClassOf", "HumanSettlement"). A knowledge graph that consist largely or wholly of structural knowledge is often called an ontology.Some knowledge graphs are general purpose, such as Wikidata and the Google knowledge graph, while others are developed for specific domains such as medicine. They are rapidly gaining in importance and are playing a key role in many applications. For example, Google uses its knowledge graph for search, question answering and Google Assistant, while Amazon and Apple also use knowledge graphs to power their personal assistants Alexa and Siri, respectively. Knowledge graphs are widely used in the domain of health and wellbeing, e.g., for organising and exchanging information and to power clinical artificial intelligence (AI). One example is FoodOn, an ontology representing food knowledge such as fine-grained food product categorization, nutrition and allergens, as well as related activities such as agriculture.Knowledge graph construction and maintenance is, however, very challenging, and may require a considerable amount of human effort. Notwithstanding the high cost of knowledge creation, knowledge graphs are often still biased, incomplete or too coarse-grained. Take HeLis, an ontology for health and lifestyle, as an example. Its food knowledge is quite simple and often represents many different variants with a single entity (e.g., "Banana" for all kinds and derivatives of bananas), and its knowledge of health is highly incomplete when compared with dedicated biomedical ontologies. In addition, it is hard to avoid errors such as incorrect facts and categorisations in knowledge graphs; e.g., FoodOn categorises soy milk as a kind of milk, but not as a kind of soy product. Such errors may be inherited from the information source or be caused by the construction procedure. These issues significantly impact the usefulness of knowledge graphs and the reliability of the systems that use them; e.g., the categorisation of soy milk could be dangerous if the knowledge graph were used in a food allergen alert system.Therefore, effective knowledge graph construction and curation is urgently required and will play a critical role in exploiting the full value of knowledge graphs. As there are now many available knowledge resources, one possible approach is to use multiple sources to address both coverage and quality issues, e.g., via integration and cross-checking. For example, integrating HeLis with FoodOn would combine fine-grained categorization of food products (including bananas) with lifestyle knowledge. Moreover, cross-checking FoodOn with HeLis will reveal the problem with soy milk, which is correctly categorized as a soy product in HeLis. Automating the integration of knowledge resources is challenging, but combining semantic and learning-based techniques seems to be a very promising approach, and we have already obtained some encouraging preliminary results in this direction.The proposed research will therefore study a range of semantic and machine learning techniques, and how to combine them to support knowledge graph construction and curation. As well as its application to knowledge graph construction and curation, this research will also contribute to the development of new neural-symbolic theories, paradigms and methods, such as deep semantic embedding for learning representations for expressive knowledge, and knowledge-guided learning for addressing sample shortage problems. These techniques promise to revolutionize many AI and big data technologies.

知识图是图结构的知识资源，其通常被表示为三元组，诸如（“UK”、“hasCapital”、“伦敦”）和（“伦敦”、“instanceOf”、“City”）。除了这些基本的“事实”，知识图通常还包括关于领域的结构知识，通常基于实体类型的层次结构（AKA类或概念）;例如，（“City”、“subClassOf”、“HumanSettlement”）。大部分或全部由结构化知识组成的知识图通常被称为本体。一些知识图是通用的，如维基数据和谷歌知识图，而另一些是为特定领域开发的，如医学。它们的重要性正在迅速增加，并在许多应用中发挥着关键作用。例如，谷歌使用其知识图谱进行搜索，问答和谷歌助手，而亚马逊和苹果也分别使用知识图谱为其个人助理Alexa和Siri提供支持。知识图谱广泛用于健康和福祉领域，例如，用于组织和交换信息，并为临床人工智能（AI）提供动力。FoodOn是一个代表食品知识的本体，如精细食品分类、营养和过敏原，以及农业等相关活动。然而，知识图的构建和维护非常具有挑战性，可能需要大量的人力。尽管知识创造的成本很高，但知识图谱通常仍然存在偏差，不完整或过于粗粒度。以HeLis为例，这是一个健康和生活方式的本体。它的食物知识非常简单，通常用一个实体代表许多不同的变体（例如，“香蕉”是指香蕉的所有种类和衍生物），与专门的生物医学本体相比，其健康知识非常不完整。此外，很难避免错误，例如知识图谱中的不正确事实和分类;例如，FoodOn将豆奶归类为一种牛奶，而不是一种豆制品。这些错误可能是从信息源继承的，也可能是由构造过程引起的。这些问题严重影响了知识图谱的有用性和使用它们的系统的可靠性;例如，知识图谱在食品过敏原预警系统中的应用，对豆浆的分类有很大的危险性，因此，有效的知识图谱构建和管理对于充分发挥知识图谱的价值至关重要。由于现在有许多可用的知识资源，一种可能的方法是使用多种来源来解决覆盖面和质量问题，例如，通过整合和交叉检查例如，将HeLis与FoodOn整合将联合收割机将食品（包括香蕉）的细粒度分类与生活方式知识结合起来。此外，交叉检查FoodOn与HeLis将揭示豆奶的问题，豆奶在HeLis中被正确归类为大豆产品。知识资源的自动化整合具有挑战性，但结合语义和基于学习的技术似乎是一个非常有前途的方法，我们已经在这个方向上取得了一些令人鼓舞的初步成果。因此，拟议的研究将研究一系列语义和机器学习技术，以及如何将它们联合收割机，以支持知识图的构建和策展。除了将其应用于知识图构建和策展之外，这项研究还将有助于开发新的神经符号理论、范式和方法，例如用于学习表达性知识表示的深度语义嵌入，以及用于解决样本短缺问题的知识引导学习。这些技术有望彻底改变许多人工智能和大数据技术。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning

DOI：
10.48550/arxiv.2207.01328
发表时间：
2022-07
期刊：
ArXiv
影响因子：
0
作者：
Zhuo Chen;Yufen Huang;Jiaoyan Chen;Yuxia Geng;Wen Zhang;Yin Fang;Jeff Z. Pan;Wenting Song;Huajun Chen
通讯作者：
Zhuo Chen;Yufen Huang;Jiaoyan Chen;Yuxia Geng;Wen Zhang;Yin Fang;Jeff Z. Pan;Wenting Song;Huajun Chen

Zero-Shot and Few-Shot Learning With Knowledge Graphs: A Comprehensive Survey

DOI：
10.1109/jproc.2023.3279374
发表时间：
2021-12
期刊：
Proceedings of the IEEE
影响因子：
20.6
作者：
Jiaoyan Chen;Yuxia Geng;Zhuo Chen;Jeff Z. Pan;Yuan He;Wen Zhang;Ian Horrocks;Hua-zeng Chen
通讯作者：
Jiaoyan Chen;Yuxia Geng;Zhuo Chen;Jeff Z. Pan;Yuan He;Wen Zhang;Ian Horrocks;Hua-zeng Chen

Contextual semantic embeddings for ontology subsumption prediction